1. Field of the Invention
The present invention relates to the technical field of vehicles, and in particular, to a lane departure warning system and method for vehicles.
2. Related Art
At the present stage, a lane detection algorithm mainly includes two steps: edge detection and line detection, which are mainly applied to detecting a manual lane marker.
In a conventional edge detection algorithm, in a general case (when there is only a simple lane marker with an obvious gradient change, and there are no other complicated situations, such as vehicles, shadows, man-made objects, and trees), a lane marker may be successfully detected and determined. However, in most cases, a real lane marker cannot be detected and determined. For example, in a case where the gradient change is not obvious, the edge detection cannot effectively detect the lane marker. In addition, in a case where light is reflected or an obstacle exists, although edge pixels can be detected, it is very difficult to identify real road marker edge pixels to determine a lane.
In many cases, although the road marker edge pixels can be correctly detected, some complicated steps are further needed to infer the lane from the edge pixels. For example, as for a wider lane marker, because the edge detection can only detect the edge pixels, pixels in the lane marker may be classified as non-edge pixels, and there are some difficulties in inferring the correct lane according to edge pixels on the left and right sides of the lane marker. In addition, as for such conditions as a short lane and a channelizing line, the conventional edge detection algorithm also cannot perform effective processing.
In most cases, the conventional lane detection algorithm belongs to a pixel-based image processing technology. After operations such as the edge detection and the line detection, only pixel-based one-dimensional (1D) information can be obtained, which cannot meet demands of the lane detection needing two-dimensional (2D) information. Therefore, some complicated procedures (for example, points are assembled into a line) are needed, to obtain enough information. However, most of these complicated procedures are time-consuming. Therefore, to improve execution efficiency of a system, many experts and research and development institutions successively put forward a strategy based on a Region Of Interest (ROI), which only performs the lane detection on reserved ROI, thereby improving the efficacy. However, in many cases, the lane may often appear in the non-ROI areas. As a result, the system cannot detect the lane.